Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/skypilot-org/skypilot

SkyPilot: Run LLMs, AI, and Batch jobs on any cloud. Get maximum savings, highest GPU availability, and managed execution—all with a simple interface.
https://github.com/skypilot-org/skypilot

cloud-computing cloud-management cost-management cost-optimization data-science deep-learning distributed-training finops gpu hyperparameter-tuning job-queue job-scheduler llm-serving llm-training machine-learning ml-infrastructure ml-platform multicloud spot-instances tpu

Last synced: 11 days ago
JSON representation

SkyPilot: Run LLMs, AI, and Batch jobs on any cloud. Get maximum savings, highest GPU availability, and managed execution—all with a simple interface.

Awesome Lists containing this project

README 

        


  

    

    SkyPilot

  






  

    Documentation

  



  

    GitHub Release

  



  

    Join Slack

  








    Run AI on Any Infra — Unified, Faster, Cheaper




----

:fire: *News* :fire:

- [Oct 2024] :tada: **SkyPilot crossed 1M+ downloads** :tada:: Thank you to our community! [**Twitter/X**](https://x.com/skypilot_org/status/1844770841718067638)

- [Sep 2024] Point, Launch and Serve **Llama 3.2** on Kubernetes or Any Cloud: [**example**](./llm/llama-3_2/)

- [Sep 2024] Run and deploy [**Pixtral**](./llm/pixtral), the first open-source multimodal model from Mistral AI.

- [Jun 2024] Reproduce **GPT** with [llm.c](https://github.com/karpathy/llm.c/discussions/481) on any cloud: [**guide**](./llm/gpt-2/)

- [Apr 2024] Serve [**Qwen-110B**](https://qwenlm.github.io/blog/qwen1.5-110b/) on your infra: [**example**](./llm/qwen/)

- [Apr 2024] Using [**Ollama**](https://github.com/ollama/ollama) to deploy quantized LLMs on CPUs and GPUs: [**example**](./llm/ollama/)

- [Feb 2024] Deploying and scaling [**Gemma**](https://blog.google/technology/developers/gemma-open-models/) with SkyServe: [**example**](./llm/gemma/)

- [Feb 2024] Serving [**Code Llama 70B**](https://ai.meta.com/blog/code-llama-large-language-model-coding/) with vLLM and SkyServe: [**example**](./llm/codellama/)

- [Dec 2023] [**Mixtral 8x7B**](https://mistral.ai/news/mixtral-of-experts/), a high quality sparse mixture-of-experts model, was released by Mistral AI! Deploy via SkyPilot on any cloud: [**example**](./llm/mixtral/)

- [Nov 2023] Using [**Axolotl**](https://github.com/OpenAccess-AI-Collective/axolotl) to finetune Mistral 7B on the cloud (on-demand and spot): [**example**](./llm/axolotl/)



**LLM Finetuning Cookbooks**: Finetuning Llama 2 / Llama 3.1 in your own cloud environment, privately: Llama 2 [**example**](./llm/vicuna-llama-2/) and [**blog**](https://blog.skypilot.co/finetuning-llama2-operational-guide/); Llama 3.1 [**example**](./llm/llama-3_1-finetuning/) and [**blog**](https://blog.skypilot.co/finetune-llama-3_1-on-your-infra/)



  Archived



- [Jul 2024] [**Finetune**](./llm/llama-3_1-finetuning/) and [**serve**](./llm/llama-3_1/) **Llama 3.1** on your infra

- [Apr 2024] Serve and finetune [**Llama 3**](https://skypilot.readthedocs.io/en/latest/gallery/llms/llama-3.html) on any cloud or Kubernetes: [**example**](./llm/llama-3/)

- [Mar 2024] Serve and deploy [**Databricks DBRX**](https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm) on your infra: [**example**](./llm/dbrx/)

- [Feb 2024] Speed up your LLM deployments with [**SGLang**](https://github.com/sgl-project/sglang) for 5x throughput on SkyServe: [**example**](./llm/sglang/)

- [Dec 2023] Using [**LoRAX**](https://github.com/predibase/lorax) to serve 1000s of finetuned LLMs on a single instance in the cloud: [**example**](./llm/lorax/)

- [Sep 2023] [**Mistral 7B**](https://mistral.ai/news/announcing-mistral-7b/), a high-quality open LLM, was released! Deploy via SkyPilot on any cloud: [**Mistral docs**](https://docs.mistral.ai/self-deployment/skypilot)

- [Sep 2023] Case study: [**Covariant**](https://covariant.ai/) transformed AI development on the cloud using SkyPilot, delivering models 4x faster cost-effectively: [**read the case study**](https://blog.skypilot.co/covariant/)

- [Jul 2023] Self-Hosted **Llama-2 Chatbot** on Any Cloud: [**example**](./llm/llama-2/)

- [Jun 2023] Serving LLM 24x Faster On the Cloud [**with vLLM**](https://vllm.ai/) and SkyPilot: [**example**](./llm/vllm/), [**blog post**](https://blog.skypilot.co/serving-llm-24x-faster-on-the-cloud-with-vllm-and-skypilot/)

- [Apr 2023] [SkyPilot YAMLs](./llm/vicuna/) for finetuning & serving the [Vicuna LLM](https://lmsys.org/blog/2023-03-30-vicuna/) with a single command!



----



SkyPilot is a framework for running AI and batch workloads on any infra, offering unified execution, high cost savings, and high GPU availability.



SkyPilot **abstracts away infra burdens**:

- Launch [dev clusters](https://skypilot.readthedocs.io/en/latest/examples/interactive-development.html), [jobs](https://skypilot.readthedocs.io/en/latest/examples/managed-jobs.html), and [serving](https://skypilot.readthedocs.io/en/latest/serving/sky-serve.html) on any infra

- Easy job management: queue, run, and auto-recover many jobs



SkyPilot **supports multiple clusters, clouds, and hardware** ([the Sky](https://arxiv.org/abs/2205.07147)):

- Bring your reserved GPUs, Kubernetes clusters, or 12+ clouds

- [Flexible provisioning](https://skypilot.readthedocs.io/en/latest/examples/auto-failover.html) of GPUs, TPUs, CPUs, with auto-retry



SkyPilot **cuts your cloud costs & maximizes GPU availability**:

* [Autostop](https://skypilot.readthedocs.io/en/latest/reference/auto-stop.html): automatic cleanup of idle resources

* [Managed Spot](https://skypilot.readthedocs.io/en/latest/examples/managed-jobs.html): 3-6x cost savings using spot instances, with preemption auto-recovery

* [Optimizer](https://skypilot.readthedocs.io/en/latest/examples/auto-failover.html): 2x cost savings by auto-picking the cheapest & most available infra



SkyPilot supports your existing GPU, TPU, and CPU workloads, with no code changes.



Install with pip:

```bash

# Choose your clouds:

pip install -U "skypilot[kubernetes,aws,gcp,azure,oci,lambda,runpod,fluidstack,paperspace,cudo,ibm,scp]"

```

To get the latest features and fixes, use the nightly build or [install from source](https://skypilot.readthedocs.io/en/latest/getting-started/installation.html):

```bash

# Choose your clouds:

pip install "skypilot-nightly[kubernetes,aws,gcp,azure,oci,lambda,runpod,fluidstack,paperspace,cudo,ibm,scp]"

```



[Current supported infra](https://skypilot.readthedocs.io/en/latest/getting-started/installation.html) (Kubernetes; AWS, GCP, Azure, OCI, Lambda Cloud, Fluidstack, RunPod, Cudo, Paperspace, Cloudflare, Samsung, IBM, VMware vSphere):



  

    

    SkyPilot

  




## Getting Started

You can find our documentation [here](https://skypilot.readthedocs.io/en/latest/).

- [Installation](https://skypilot.readthedocs.io/en/latest/getting-started/installation.html)

- [Quickstart](https://skypilot.readthedocs.io/en/latest/getting-started/quickstart.html)

- [CLI reference](https://skypilot.readthedocs.io/en/latest/reference/cli.html)



## SkyPilot in 1 Minute



A SkyPilot task specifies: resource requirements, data to be synced, setup commands, and the task commands.



Once written in this [**unified interface**](https://skypilot.readthedocs.io/en/latest/reference/yaml-spec.html) (YAML or Python API), the task can be launched on any available cloud.  This avoids vendor lock-in, and allows easily moving jobs to a different provider.



Paste the following into a file `my_task.yaml`:



```yaml

resources:

  accelerators: A100:8  # 8x NVIDIA A100 GPU



num_nodes: 1  # Number of VMs to launch



# Working directory (optional) containing the project codebase.

# Its contents are synced to ~/sky_workdir/ on the cluster.

workdir: ~/torch_examples



# Commands to be run before executing the job.

# Typical use: pip install -r requirements.txt, git clone, etc.

setup: |

  pip install "torch<2.2" torchvision --index-url https://download.pytorch.org/whl/cu121



# Commands to run as a job.

# Typical use: launch the main program.

run: |

  cd mnist

  python main.py --epochs 1

```



Prepare the workdir by cloning:

```bash

git clone https://github.com/pytorch/examples.git ~/torch_examples

```



Launch with `sky launch` (note: [access to GPU instances](https://skypilot.readthedocs.io/en/latest/cloud-setup/quota.html) is needed for this example):

```bash

sky launch my_task.yaml

```



SkyPilot then performs the heavy-lifting for you, including:

1. Find the lowest priced VM instance type across different clouds

2. Provision the VM, with auto-failover if the cloud returned capacity errors

3. Sync the local `workdir` to the VM

4. Run the task's `setup` commands to prepare the VM for running the task

5. Run the task's `run` commands





  SkyPilot Demo




Refer to [Quickstart](https://skypilot.readthedocs.io/en/latest/getting-started/quickstart.html) to get started with SkyPilot.



## More Information

To learn more, see our [documentation](https://skypilot.readthedocs.io/en/latest/), [blog](https://blog.skypilot.co/), and [community integrations](https://blog.skypilot.co/community/).



Runnable examples:

- LLMs on SkyPilot

  - [Llama 3.2: lightweight and vision models](./llm/llama-3_2/)

  - [Pixtral](./llm/pixtral/)

  - [Llama 3.1 finetuning](./llm/llama-3_1-finetuning/) and [serving](./llm/llama-3_1/)

  - [GPT-2 via `llm.c`](./llm/gpt-2/)

  - [Llama 3](./llm/llama-3/)

  - [Qwen](./llm/qwen/)

  - [Databricks DBRX](./llm/dbrx/)

  - [Gemma](./llm/gemma/)

  - [Mixtral 8x7B](./llm/mixtral/); [Mistral 7B](https://docs.mistral.ai/self-deployment/skypilot/) (from official Mistral team)

  - [Code Llama](./llm/codellama/)

  - [vLLM: Serving LLM 24x Faster On the Cloud](./llm/vllm/) (from official vLLM team)

  - [SGLang: Fast and Expressive LLM Serving On the Cloud](./llm/sglang/) (from official SGLang team)

  - [Vicuna chatbots: Training & Serving](./llm/vicuna/) (from official Vicuna team)

  - [Train your own Vicuna on Llama-2](./llm/vicuna-llama-2/)

  - [Self-Hosted Llama-2 Chatbot](./llm/llama-2/)

  - [Ollama: Quantized LLMs on CPUs](./llm/ollama/)

  - [LoRAX](./llm/lorax/)

  - [QLoRA](https://github.com/artidoro/qlora/pull/132)

  - [LLaMA-LoRA-Tuner](https://github.com/zetavg/LLaMA-LoRA-Tuner#run-on-a-cloud-service-via-skypilot)

  - [Tabby: Self-hosted AI coding assistant](https://github.com/TabbyML/tabby/blob/bed723fcedb44a6b867ce22a7b1f03d2f3531c1e/experimental/eval/skypilot.yaml)

  - [LocalGPT](./llm/localgpt)

  - [Falcon](./llm/falcon)

  - Add yours here & see more in [`llm/`](./llm)!

- Framework examples: [PyTorch DDP](https://github.com/skypilot-org/skypilot/blob/master/examples/resnet_distributed_torch.yaml), [DeepSpeed](./examples/deepspeed-multinode/sky.yaml), [JAX/Flax on TPU](https://github.com/skypilot-org/skypilot/blob/master/examples/tpu/tpuvm_mnist.yaml), [Stable Diffusion](https://github.com/skypilot-org/skypilot/tree/master/examples/stable_diffusion), [Detectron2](https://github.com/skypilot-org/skypilot/blob/master/examples/detectron2_docker.yaml), [Distributed](https://github.com/skypilot-org/skypilot/blob/master/examples/resnet_distributed_tf_app.py) [TensorFlow](https://github.com/skypilot-org/skypilot/blob/master/examples/resnet_app_storage.yaml), [Ray Train](examples/distributed_ray_train/ray_train.yaml), [NeMo](https://github.com/skypilot-org/skypilot/blob/master/examples/nemo/nemo.yaml), [programmatic grid search](https://github.com/skypilot-org/skypilot/blob/master/examples/huggingface_glue_imdb_grid_search_app.py), [Docker](https://github.com/skypilot-org/skypilot/blob/master/examples/docker/echo_app.yaml), [Cog](https://github.com/skypilot-org/skypilot/blob/master/examples/cog/), [Unsloth](https://github.com/skypilot-org/skypilot/blob/master/examples/unsloth/unsloth.yaml), [Ollama](https://github.com/skypilot-org/skypilot/blob/master/llm/ollama), [llm.c](https://github.com/skypilot-org/skypilot/tree/master/llm/gpt-2), [Airflow](./examples/airflow/training_workflow) and [many more (`examples/`)](./examples).



Case Studies and Integrations: [Community Spotlights](https://blog.skypilot.co/community/)



Follow updates:

- [Twitter](https://twitter.com/skypilot_org)

- [Slack](http://slack.skypilot.co)

- [SkyPilot Blog](https://blog.skypilot.co/) ([Introductory blog post](https://blog.skypilot.co/introducing-skypilot/))



Read the research:

- [SkyPilot paper](https://www.usenix.org/system/files/nsdi23-yang-zongheng.pdf) and [talk](https://www.usenix.org/conference/nsdi23/presentation/yang-zongheng) (NSDI 2023)

- [Sky Computing whitepaper](https://arxiv.org/abs/2205.07147)

- [Sky Computing vision paper](https://sigops.org/s/conferences/hotos/2021/papers/hotos21-s02-stoica.pdf) (HotOS 2021)

- [Policy for Managed Spot Jobs](https://www.usenix.org/conference/nsdi24/presentation/wu-zhanghao)  (NSDI 2024)



## Support and Questions

We are excited to hear your feedback!

* For issues and feature requests, please [open a GitHub issue](https://github.com/skypilot-org/skypilot/issues/new).

* For questions, please use [GitHub Discussions](https://github.com/skypilot-org/skypilot/discussions).



For general discussions, join us on the [SkyPilot Slack](http://slack.skypilot.co).



## Contributing

We welcome all contributions to the project! See [CONTRIBUTING](CONTRIBUTING.md) for how to get involved.